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Objective: This cross-sectional study aimed to distinguish patients with mild cognitive impairment (MCI) from patients with normal controls (NCs) by measuring the levels of N-acetyl aspartate (NAA), total creatinine (tCr), and choline (Cho) in their hippocampus (HIP) and their posterior cingulate gyrus (PCG) by using proton magnetic resonance spectroscopy (MRS) and to predict the cutoff value on the ratios of metabolites. We further aimed to provide a reference for the diagnosis of MCI in elderly patients in China.Methods: About 69 patients who underwent a clinical diagnosis of the MCI group and 67 patients with NCs, the Mini-Mental Status Examination (MMSE) score, the Montreal Cognitive Assessment (MoCA) score, and MRS of the bilateral HIP and bilateral PCG were considered. The ratio of NAA/tCr and Cho/tCr in the bilateral HIP and bilateral PCG was calculated. The relationship between the ratios of metabolites and the scores of MMSE and MoCA was analyzed, and the possible brain metabolite cutoff point for the diagnosis of MCI was evaluated.Results: Compared with the NC group, the scores of MMSE and MoCA in the MCI group decreased significantly (p < 0.05); the ratio of NAA/tCr in the bilateral HIP and bilateral PCG and the ratio of Cho/tCr at the right HIP in the MCI group decreased significantly (p < 0.05); however, there was no significant difference in the ratio of Cho/tCr in the left HIP and bilateral PCG between the two groups (p > 0.05). The correlation coefficient between MMSE/MoCA and the ratio of NAA/tCr was 0.49–0.56 in the bilateral HIP (p < 0.01). The best cutoff value of NAA/creatine (Cr) in the left HIP and the right HIP was 1.195 and 1.19. Sensitivity, specificity, and the Youden index (YDI) in the left HIP and the right HIP were (0.725, 0.803, 0.528) and (0.754, 0.803, 0.557), respectively.Conclusion: The level of metabolites in the HIP and the PCG of patients with MCI and of those with normal subjects has a certain correlation with the score of their MMSE and MoCA. When the value of NAA/tCr in the left HIP and right HIP is

Xiangming Deng,1 Jinlong Teng,1 Xiucheng Nong,1 Bihan Yu,1 Liying Tang,2,* Jinsong Liang,3 Zhuocheng Zou,1,* Qiang Liu,2,* Lu Zhou,4 Qirong Li,5 Lihua Zhao1 1Department of Acupuncture and Moxibustion, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, People’s Republic of China; 2The Xinhu Outpatient Clinic of the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, People’s Republic of China; 3Department of Clinical Laboratory of the First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, People’s Republic of China; 4Medical Examination Center of the First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, People’s Republic of China; 5Renai Branch of the First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lihua ZhaoDepartment of Acupuncture and Moxibustion, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Dongge Road & 89-9, Nanning, 530023, People’s Republic of ChinaTel +86 130 06913108Email zhaolh67@163.comQirong LiDepartment of Acupuncture and Moxibustion, Renai Branch of the First Affiliated Hospital of Guangxi University of Chinese Medicine, Mingxiu East Road & 179, Nanning, 530023, People’s Republic of ChinaTel +86 136 07717755Email 632481428@qq.comIntroduction: The incidence of Alzheimer’s disease is on the rise, early detection of cognitive impairment of the elderly is very important. In traditional Chinese medicine, constitution is related to the susceptibility of the human body to diseases. Based on the theory of constitution of traditional Chinese medicine (TCM), the human population can be classified into 9 constitutions. However, little is known about the characteristics of medical constitution and related biomarkers in subjects with mild cognitive impairment (MCI).Methods: We measured the TCM Constitution of 214 subjects by using the Constitution in Chinese Medicine Questionnaire (CCMQ). MMSE and MoCA were used to assess cognitive function. The subjects were divided into mild cognitive impairment group (MCI, n = 152) and normal control group (NC, n = 62). The levels of serum Hcy and serum/urine 8-iso-PGF 2α were determined.Results: 1) It was found that there was a significant difference in constitution types between MCI and NC. There were significant differences in MMSE and MoCA score, serum Hcy and serum/urine 8-iso-PGF 2a levels between the two groups. 2) In logistic regression analysis, the variables with statistical significance were TCM Constitution of Yang-Deficient, Phlegm-Dampness, Blood-Stasis and abnormal increase of Hcy (OR> 1). 3) The MoCA scores had a positive correlation with the MMSE. A statistically significant inverse association was found between serum Hcy, blood and urine 8-iso-PGF 2a and scores of cognitive assessment in MCI.Conclusion: Constitution types (Yang-Deficient, Phlegm-Dampness and Blood-Stasis) and abnormal serum Hcy elevation can be used as risk factors for MCI. MoCA scores can serve to detect MCI at early stage. Serum/urine 8-iso-PGF 2α has a certain relationship with MCI. Higher levels of serum/urine 8-iso–PGF 2α are more likely to be associated with MCI risk.Keywords: cognitive impairment, constitutions of traditional Chinese medicine, biomarkers, MoCA score

Background The brain’s dynamic spontaneous neural activity and dynamic functional connectivity (dFC) are both important in supporting cognition, but how these two types of brain dynamics evolve and co-evolve in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remain unclear. The aim of the present study was to investigate recurrent and concurrent patterns of two types of dynamic brain states correlated with cognitive decline. Methods The present study analyzed resting-state functional magnetic resonance imaging data from 62 SCD patients, 75 MCI patients, and 70 healthy controls (HCs). We used the sliding-window and clustering method to identify two types of recurrent brain states from both dFC and dynamic regional spontaneous activity, as measured by dynamic fractional amplitude of low-frequency fluctuations (dfALFF). Then, the occurrence frequency of a dFC or dfALFF state and the co-occurrence frequency of a pair of dFC and dfALFF states among all time points are extracted for each participant to describe their dynamics brain patterns. Results We identified a few recurrent states of dfALFF and dFC and further ascertained the co-occurrent patterns of these two types of dynamic brain states (i.e., dfALFF and dFC states). Importantly, the occurrence frequency of a default-mode network (DMN)-dominated dFC state was significantly different between HCs and SCD patients, and the co-occurrence frequencies of a DMN-dominated dFC state and a DMN-dominated dfALFF state were also significantly different between SCD and MCI patients. These two dynamic features were both significantly positively correlated with Mini-Mental State Examination scores. Conclusion Our findings revealed novel fMRI-based neural signatures of cognitive decline from recurrent and concurrent patterns of dfALFF and dFC, providing strong evidence supporting SCD as the transition phase between normal aging and MCI. This finding holds potential to differentiate SCD patients from HCs via both dFC and dfALFF as objective neuroimaging biomarkers, which may aid in the early diagnosis and intervention of Alzheimer’s disease.

Subjective cognitive decline (SCD) is an early stage of mild cognitive impairment (MCI) and may represent the first symptom manifestation of Alzheimer’s disease (AD). Early diagnosis of MCI is important because early identification and intervention can delay or even reverse the progression of this disease. This paper proposes an automatic diagnostic framework for SCD and MCI. Specifically, we design a new multi-task learning model to integrate neuroimaging functional and structural connectivity in a predictive framework. We construct a functional brain network by sparse low-rank brain network estimation methods, and a structural brain network is constructed using fiber bundle tracking. Subsequently, we use multi-task learning methods to select features for integrated functional and structural connections, the importance of each task and the balance between both modalities are automatically learned. By integrating both functional and structural information, the most discriminative features of the disease are obtained for diagnosis. The experiments on the dataset show that our proposed method achieves good performance and is superior to the traditional algorithms. In addition, the proposed method can identify the most discriminative brain regions and connections. These results follow current clinical findings and add new findings for disease detection and future medical analysis.